Insights into pyrolytic feedstock potential of date palm industry wastes: Kinetic study and product characterization

Assessment of the thermal degradation kinetics of fresh green coconut husk (Cocos nucifera L.) powder and after methyl levulinate production

“…The composition of biomass is one of the most relevant factors in identifying its energy potential in order to guarantee its application as a source of sustainable energy [24]. The thermogravimetric technique was used in the samples (CHP, CHP-AS and CHP-TD) to obtain the content of the main constituents of green CHP (Table 1).…”

“…Isoconversional methods have been widely used to determine the non-kinetic parameters in pyrolysis processes and conversion reactions of solid raw materials and biomass [24,25,26]. It is presumed that the activation energies of kinetic degradation reactions are represented by activation energy distribution as a function of conversion degree [27].…”

Combustion of Biomass in Fluidized Beds: A Review of Key Phenomena and Future Perspectives

“…For the inverse situation, i.e., determining A ( E ) and g ( E ) from pyrolysis experiments, the problem is mathematically ill posed, thus unsolvable, except for simplified cases where the number of reactions is finite and the reactions are independent and do not overlap, as discussed by Scott et al One of the common approaches to overcome the ill-posed problem is to assume the type of the distribution that describes g ( E ); for instance, often, a Gaussian distribution is taken, adjusting the mean and the standard deviation of g ( E ) to fit the model to measurements. ,,− As a side note, the CPD model described earlier usually implements a Gaussian distribution as well . For DAEM and its application for determining pyrolysis kinetics, an algorithm was developed by Scott et al and demonstrated on pyrolysis of sewage sludge in a fluidized bed and later in a range of various types of unconventional, so often difficult to handle and model, fuels, such as date palm waste or microalgae . Recently, a two-dimensional DAEM was developed by considering the pre-exponential factor, A , as a separate variable, independent of E , and the distribution function is two-dimensional, g ( A , E ), depending both on A and E .…”

“…65,93,95−98 As a side note, the CPD model described earlier usually implements a Gaussian distribution as well. 99 For DAEM and its application for determining pyrolysis kinetics, an algorithm was developed by Scott et al 94 and demonstrated on pyrolysis of sewage sludge in a fluidized bed 100 and later in a range of various types of unconventional, so often difficult to handle and model, fuels, such as date palm waste 101 or microalgae. 102 Recently, a twodimensional DAEM was developed by considering the preexponential factor, A, as a separate variable, independent of E, and the distribution function is two-dimensional, g(A, E), depending both on A and E. The justification is that the relationship between A and E is unclear; hence, treating them independently will help overcome problems with 1D DAEM, caused, for example, by the compensation effect.…”

Sustainable Valorization of Olive Pomace Waste to Renewable Biofuels, Biomaterials and Biochemicals Via Pyrolysis Process: Experimental and Numerical Investigation

“…Within the devolatilization zone, it can be seen that two zones could be observed: the first between 180 and 340 °C that involves the hemicelluloses and cellulose degradation. The lignins decomposition starts after 350 °C with a slow rate (Bensidhom et al 2021), more it gradually debases up to the ultimate pyrolysis temperature of 600 °C. After this temperature, the passive zone started where the devolatilization practically finished and there is no loss of additional mass.…”